code: def crop_scope(
      self, x, yin_start, scope_shift
  ):  # x: tensor [B,C,T] #scope_shift: tensor [B]
      return torch.stack(
          [
              x[
                  i,
                  yin_start
                  + scope_shift[i] : yin_start
                  + self.yin_scope
                  + scope_shift[i],
                  :,
              ]
              for i in range(x.shape[0])
          ],
          dim=0,
      )
  
file_path: espnet/espnet2/tts/feats_extract/ying.py
imports: import math
  from typing import Any, Dict, Tuple, Union
  import torch
  from espnet2.tts.feats_extract.abs_feats_extract import AbsFeatsExtract
  from espnet2.tts.feats_extract.yin import *
  from espnet.nets.pytorch_backend.nets_utils import pad_list
label_desc: Use image node_id 10 for calling the Ying obj's underlying member method code with example usage: obj.crop_scope(x, yin_start, scope_shift) and returns: torch